Verified
1 
IdentitySkills
Skill Set
1. Programming Skills:
ā Java (Basic to Intermediate + OOP Concepts, Multithreading Programming)
ā VBA Programming (Scripting, Reporting Automations)
ā Visual Basic Programming
ā Python Programming
ā C Programming (Data Structures, Multithreading with Mutexes)
2. Spreadsheets:
ā MS Excel Expert: Power Pivot, Reporting, Power Query, Automated
Consolidations, Dashboarding, Advanced and Basic Excel Formulas, VBA
Automation, VBA Web Scraping Capabilities
ā Google Sheets: Reporting, GAS Automation (with ChatGPT assistance)
3. Presentation Tools:
ā PowerPoint Presentation
ā PowerPoint Data Entry Automation
4. Command Line:
ā Windows CMD Basic Commands: mkdir, cd, tree, tree /f, start calc,
start excel, compiling Python and Java programs, ipconfig
5. Theoretical and Practical Knowledge:
ā Networking Basics:
ā Network Topology, OSI Model (and all OSI Protocols), IPv4, IPv6
ā Leaky Bucket and Sliding Window Algorithms (Implemented in C, Java,
Python, VBA)
ā Operating System Concepts:
ā Memory Management Algorithms: FIFO, LIFO, LRU, Page Fault, Demand
Paging
ā Segmentation Fault, Physical Address, Logical Address
ā Statistics:
ā Moments (First, Second, Third, Fourth) and Factorial Moments
ā Mean, Median, Mode, Kurtosis, Skewness
ā Correlation, Association between Variables, Linear Regression
(Implemented from Scratch)
ā Database Concepts (SQL):
ā Normalization (All Forms), SQL Queries, Joins, String Functions,
Constraints (Referential Integrity, Primary Key, NOT NULL, Unique)
ā Data Visualization Tools:
ā Tableau: Calculated Columns, Data Visualization
ā Power BI: DAX, Power Query, Data Visualization
Projects Done
1. MoneyControl Python Web Scraper:
ā Automated data analysis and created a Python-based web scraper for
MoneyControl.
ā Workflow:
ā Collected data ā Loaded into Power Query ā Consolidated ā
Cleaned in Excel ā Created measures and calculated
columns in DAX (Power Pivot).
ā Identified stocks gaining popularity (low to high mutual fund picks).
ā Created PPT with charts for stock price, popularity, and mutual fund
investment trends over a year, with data collected weekly on Mondays.
2. Amazon Sourcing Business Project:
ā Automated dynamic data analysis for Amazon product trends.
ā Workflow:
ā Manually collected data from SaaS platforms (Helium 10, Jungle Scout).
ā Loaded into Power Query ā Consolidated ā Performed
analysis using DAX in Power Pivot (Excel).
ā Identified trending Amazon products for any time period dynamically.
Common Statistical Techniques Used in Projects:
ā Calculated the standard deviation of total sales (or mutual fund picks) grouped by data
collection dates.
ā Derived statistical range, last week vs. first week differences, and normalized these
metrics.
ā Summed normalized values to compute a composite score, sorted by largest to smallest
for rank-based output.
ā Delivered a fully automated and dynamic data analysis process for both projects
Sector 12,
Noida
Certified
